A computational challenge problem in materials discovery: synthetic problem generator and real-world datasets
@inproceedings{LeBras2014ACC, title={A computational challenge problem in materials discovery: synthetic problem generator and real-world datasets}, author={Ronan Le Bras and Richard Bernstein and J. Gregoire and Santosh K. Suram and Carla P. Gomes and Bart Selman and Robert Bruce van Dover}, booktitle={AAAI Conference on Artificial Intelligence}, year={2014} }
Newly-discovered materials have been central to recent technological advances. They have contributed significantly to breakthroughs in electronics, renewable energy and green buildings, and overall, have promoted the advancement of global human welfare. Yet, only a fraction of all possible materials have been explored. Accelerating the pace of discovery of materials would foster technological innovations, and would potentially address pressing issues in sustainability, such as energy production…
15 Citations
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References
SHOWING 1-10 OF 18 REFERENCES
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
- Materials Science
- 2013
Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core…
SMT-Aided Combinatorial Materials Discovery
- Computer ScienceSAT
- 2012
This work describes a novel approach to the phase map identification problem where domain-specific scientific background knowledge about the physical and chemical properties of the materials are integrated into an SMT reasoning framework and shows that it provides accurate and physically meaningful interpretations of the data, even in the presence of artificially added noise.
The high-throughput highway to computational materials design.
- Materials ScienceNature materials
- 2013
A current snapshot of high-throughput computational materials design is provided, and the challenges and opportunities that lie ahead are highlighted.
Rapid identification of structural phases in combinatorial thin-film libraries using x-ray diffraction and non-negative matrix factorization.
- Computer ScienceThe Review of scientific instruments
- 2009
The use of NMF is applied to the problem of analyzing hundreds of x-ray microdiffraction patterns from a combinatorial materials library to reduce the arduous task to the much smaller task of identifying only nine microXRD patterns.
Rapid structural mapping of ternary metallic alloy systems using the combinatorial approach and cluster analysis.
- Materials ScienceThe Review of scientific instruments
- 2007
The arduous analysis and classification of hundreds of spectra is reduced to a much shorter analysis of only a few spectra in a procedure for the quick identification of structural phases in thin film composition spread experiments.
Constraint Reasoning and Kernel Clustering for Pattern Decomposition with Scaling
- Computer ScienceCP
- 2011
This work proposes a Constraint Programming (CP) model which captures the exact problem structure yet fails to scale in the presence of noisy data about the patterns, and employs Machine Learning techniques to decompose the problem into smaller ones based on a global data-driven view.
Combinatorial Synthesis and Evaluation of Functional Inorganic Materials Using Thin-Film Techniques
- Chemistry
- 2002
Novel phases of functional inorganic chemical systems can be efficiently explored using high-throughput thin-film fabrication techniques coupled with rapid characterization schemes. High-throughput…
Crowdsourcing Backdoor Identification for Combinatorial Optimization
- Computer ScienceIJCAI
- 2013
This work shows how human computation insights can be key to identifying so-called backdoor variables in combinatorial optimization problems, and leverages the complementary strength of human input, based on a visual identification of problem structure, crowdsourcing, and the power of combinatorsial solvers to exploit complex constraints.
High energy x-ray diffraction/x-ray fluorescence spectroscopy for high-throughput analysis of composition spread thin films.
- PhysicsThe Review of scientific instruments
- 2009
This work presents a technique for simultaneous acquisition of diffraction images and fluorescence spectra on a continuous composition spread thin film using a 60 keV x-ray source, which affords direct comparison of the measured profiles with powder patterns of known phases.